2,181 research outputs found
VANET Applications: Hot Use Cases
Current challenges of car manufacturers are to make roads safe, to achieve
free flowing traffic with few congestions, and to reduce pollution by an
effective fuel use. To reach these goals, many improvements are performed
in-car, but more and more approaches rely on connected cars with communication
capabilities between cars, with an infrastructure, or with IoT devices.
Monitoring and coordinating vehicles allow then to compute intelligent ways of
transportation. Connected cars have introduced a new way of thinking cars - not
only as a mean for a driver to go from A to B, but as smart cars - a user
extension like the smartphone today. In this report, we introduce concepts and
specific vocabulary in order to classify current innovations or ideas on the
emerging topic of smart car. We present a graphical categorization showing this
evolution in function of the societal evolution. Different perspectives are
adopted: a vehicle-centric view, a vehicle-network view, and a user-centric
view; described by simple and complex use-cases and illustrated by a list of
emerging and current projects from the academic and industrial worlds. We
identified an empty space in innovation between the user and his car:
paradoxically even if they are both in interaction, they are separated through
different application uses. Future challenge is to interlace social concerns of
the user within an intelligent and efficient driving
Real-time automated road, lane and car detection for autonomous driving
In this paper, we discuss a vision based system for autonomous guidance of vehicles. An autonomous intelligent vehicle has to perform a number of
functionalities. Segmentation of the road, determining the boundaries to drive in and recognizing the vehicles and obstacles around are the main tasks for vision guided vehicle navigation. In this article we propose a set of algorithms which lead to the solution of road and vehicle segmentation using data from a color camera. The algorithms described here combine gray value difference
and texture analysis techniques to segment the road from the image, several geometric transformations and contour processing algorithms are used to segment lanes, and moving cars are extracted with the help of background modeling and estimation. The techniques developed have been tested in real road images and the results are presented
Simple Baseline for Vehicle Pose Estimation: Experimental Validation
Significant progress on human and vehicle pose estimation has been achieved in recent years. The performance of these methods has evolved from poor to remarkable in just a couple of years. This improvement has been obtained from increasingly complex architectures. In this paper, we explore the applicability of simple baseline methods by adding a few deconvolutional layers on a backbone network to estimate heat maps that correspond to the vehicle keypoints. This approach has been proven to be very effective for human pose estimation. The results are analyzed on the PASCAL3DC dataset, achieving state-of-the-art results. In addition, a set of experiments has been conducted to study current shortcomings in vehicle keypoints labelling, which adversely affect performance. A new strategy for de ning vehicle keypoints is presented and validated with our customized dataset with extended keypoints
A Recording and Analysis System of Bioptic Driving Behaviors
Millions of visually impaired people do not drive because they fail to meet the general vision requirements. There is a legal option in 38 US states where people with moderate central vision loss (e.g. visual acuity better than 20/200) may be permitted to drive while wearing spectacle-mounted bioptic telescopes. However, the safety of bioptic driving is still highly controversial, because bioptic use in driving is not well understood. Whether and how bioptic telescopes are actually used in driving, how they should be used appropriately, and whether their use results in better or worse driving performance has never been scientifically established. We are developing an in-car camera system that can be installed in bioptic drivers’ own vehicles to record their daily driving activities over long periods of time. Videos of the driver and traffic, GPS coordinates, XYZ acceleration, and vehicle black box data are recorded. We are also developing computer-aided reviewing techniques to automatically identify the most informative driving segments from the vast amount of data and, reconstruct the selected driving maneuvers on an interactive interface, so that these representative segments can be assessed off-line by driver evaluation and training specialists
Bayesian inference application to burglary detection
Real time motion tracking is very important for video analytics. But very little research has been done in identifying the top-level plans behind the atomic activities evident in various surveillance footages [61]. Surveillance videos can contain high level plans in the form of complex activities [61]. These complex activities are usually a combination of various articulated activities like breaking windshield, digging, and non-articulated activities like walking, running. We have developed a Bayesian framework for recognizing complex activities like burglary. This framework (belief network) is based on an expectation propagation algorithm [8] for approximate Bayesian inference. We provide experimental results showing the application of our framework for automatically detecting burglary from surveillance videos in real time
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